*** Please note: The deadline for this call has now passed. Thank you to all the applicants and for your interest. The below information is being left up for reference.***
Up to 8 months
October 14, 2022
Proposals are to be submitted using an online application form
The Data Sciences Institute (DSI) is a central hub and incubator for data science research, training, and partnerships at the University of Toronto. Its goal is to accelerate the impact of data sciences across disciplines to address pressing societal questions and to drive positive social change.
The DSI Seed Funding for Methodologists initiative supports single applicants working in data sciences methodology or theory. In applying for this grant, applicants agree to (a) present their work to an audience of applied researchers and (b) apply for a Catalyst Grant with a new Collaborative Research Team (CRT). Ideal candidates will have a novel methodological or theoretical tool that has potential uses in a variety of applications.
The purpose of this grant is to catalyse new Collaborative Research Teams by encouraging new collaborations of data science methodologists and theorists with applied researchers. By presenting and bringing to the fore innovative methodological and theoretical work, our goal is to spotlight exciting methodological innovations and facilitate new and unexpected connections between data science methodologists and applied researchers to foment cutting edge data science work.
An applicant’s research area should focus on data sciences methodology or theory with the potential to be relevant to applied fields. Applicants should summarize their innovative data sciences work and explain its relevance and potential for engaging applied fields.
If successful, applicants will present their work and funds of up to $10,000 can be used to seed a new Collaborative Research Team with the aim of applying for a DSI Catalyst Grant. Funds can be used for up to eight months to support that team through the application process. The DSI will fund five applicants each year and will hold calls twice yearly until our funding is used.
Successful applicants are required to:
In addition, awardees may be called upon to act as reviewers for future DSI awards competitions.
The DSI is strongly committed to diversity within its community and especially welcomes applications from racialized persons / persons of colour, women, Indigenous / Aboriginal People of North America, persons with disabilities, LGBTQ2S+ persons, and others who may contribute to the further diversification of ideas.
The award is open to applicants who meet the following criteria:
*Faculty budgetary appointments for the University of Toronto are continuing, full-time academic appointments with salary commitments from a University of Toronto academic unit.
Applicants apply online and include the following information.
PDF Application Form that includes:
Applications should be submitted to the DSI Office: firstname.lastname@example.org
Email subject line: Last name, First Name DSI SFM APPLICATION
Please submit your application as one merged PDF file that includes your application form and CV, titled [Last name], [First name] – SFM Application.pdf.
Members of the DSI’s Research & Academics Committee will review eligible proposals received by the submission deadline.
Applications will be evaluated on the innovative methodology/theory as well as the description of its potential for use in applied domains.
Notification: Decisions will be reported by end of October 2022
Jessica Gronsbell (Department of Statistical Sciences, Faculty of Arts and Science): “Infairness: Algorithmic bias evaluation and mitigation for large unlabeled datasets with broad application”
Joseph Jay Williams (Department of Computer Science, Faculty of Arts and Science): “SMART Systems: Dynamic self-optimizing system based on user input for time-sensitive applications”
Ting Kam Leonard Wong (Department of Computer and Mathematical Sciences, University of Toronto Scarborough): “Macroscopic Models of Equity Markets and Portfolio Selection”
Murat Erdogdu (Department of Computer Science, Faculty of Arts & Science): “Applications of Stein’s Method in ML”
Aya Mitani (Dalla Lana School of Public Health): “Matrix-Variate Regression for Multilevel Data”
Linbo Wang (Department of Computer & Mathematical Sciences, University of Toronto Scarborough): “Causal Inference: From Prediction to Actionable Insights”